Graph convolutional network approach to investigate potential selective Limk1 inhibitors.

Zhong, Weihe; Zhao, Lu; Yang, Ziduo; Yu-Chian Chen, Calvin

Zhong, Weihe; Zhao, Lu; Yang, Ziduo; Yu-Chian Chen, Calvin.

Afiliação

Zhong W; Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, 510275, China; School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong, 510275, China.
Zhao L; Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, 510275, China; Department of Clinical Laboratory, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, 510655, China.
Yang Z; Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, 510275, China.
Yu-Chian Chen C; Artificial Intelligence Medical Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, Guangdong, 510275, China; Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Bioinformatics and Medical Engineering, Asia Univer

J Mol Graph Model ; 107: 107965, 2021 09.

Article em En | MEDLINE | ID: mdl-34167067

Assuntos

Simulação de Dinâmica Molecular; Ligantes; Simulação de Acoplamento Molecular

Palavras-chave

Artificial intelligence (AI); Graph convolutional network (GCN); LIM kinase 1 (Limk1); Molecular docking; Molecular dynamics (MD) simulation

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Texto completo: 1 Base de dados: MEDLINE Medicinas Tradicionais: Medicinas_tradicionales_de_asia / Medicina_china Assunto principal: Simulação de Dinâmica Molecular Tipo de estudo: Prognostic_studies Idioma: En Revista: J Mol Graph Model Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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